The precision of case difficulty and referral decisions: an innovative automated approach.

OBJECTIVES: Endodontic treatment works as a successful treatment modality in several cases. However, it may fail due to some reasons unforeseeable by the dentist. Many failures can be prevented by carefully assessing the difficulty level of the case before initiating treatment or by referral to a specialist. This study presents an approach using machine learning to generate an algorithm which can help predict the difficulty level of the case and decide about a referral, with the help of the standard American Association of Endodontists (AAE) Endodontic Case Difficulty Assessment Form. MATERIALS AND METHODS: Using the AAE Endodontic Case Difficulty Form after obtaining the patients' consent, 500 potential root canal patients were diagnosed. The filled forms were assessed by two pre-calibrated endodontists, and, in cases of conflicting opinion, a third endodontist's opinion was taken. Artificial neural network was used for generating the algorithm. RESULTS: Using 500 filled AAE forms, a sensitivity of 94.96% was achieved by the machine learning algorithm. CONCLUSION: This study provides an option for automation to the conventional method of predicting the difficulty level of a case, thus increasing the speed of decision-making and referrals if necessary. CLINICAL RELEVANCE: An AAE Endodontic Case Difficulty Assessment Form when utilized along with machine learning can assist general dentists in rapid assessment of the case difficulty. This is a helpful tool in developing countries, where endodontic treatment and referral guidelines are often neglected. It also helps to make difficulty level assessments easier for novice practitioners, when they are in doubt about the same.

Low Prevalence of Amalgam-Associated Lichenoid Lesions in the Oral Cavity: A Prospective Study.

INTRODUCTION: Amalgam has been the restoration of choice for years, but its popularity has declined due to concerns about aesthetics, mercury toxicity and lichenoid lesions associated with it. Lichenoid reaction is considered to be a delayed hypersensitivity type of reaction and it has been associated with dental materials in general and amalgam in particular. MATERIALS AND METHODOLOGY: Two thousand patients having at least one amalgam restoration were examined for signs of lichenoid lesions when visiting the OPD of Conservative Dentistry and Endodontics at the Nair Hospital Dental College in Mumbai, India. Indirect spatial correlation to the amalgam restoration and the same were recorded. Descriptive analysis was used. RESULTS: Three (0.15%) out of 2000 patients with amalgam-associated lichenoid lesions showed complete resolution of lesions after the replacement of the restorations. CONCLUSION: Amalgam associated lichenoid lesions have a low prevalence and should not be a contraindication to its use in routine restorative dental practice. Patch tests and biopsies have questionable diagnostic and prognostic value. Identification of the lesions should be made after the elimination of all other causative factors for the presenting symptoms. A close spatial association of the lesion to amalgam and the regression of symptoms after its removal should be considered as confirming the diagnosis.